Intent-specific automatic speech recognition result generation

ABSTRACT

Features are disclosed for generating intent-specific results in an automatic speech recognition system. The results can be generated by utilizing a decoding graph containing tags that identify portions of the graph corresponding to a given intent. The tags can also identify high-information content slots and low-information carrier phrases for a given intent. The automatic speech recognition system may utilize these tags to provide a semantic representation based on a plurality of different tokens for the content slot portions and low information for the carrier portions. A user can be presented with a user interface containing top intent results with corresponding intent-specific top content slot values.

BACKGROUND

Spoken language processing systems can process audio data of spoken userinput to generate one or more possible transcriptions of what the usersaid. Spoken language processing systems can then identify the meaningof what the user said in order to take some action in response to thespoken input from the user. Some spoken language processing systemscontain an automatic speech recognition (“ASR”) module that may generateone or more likely transcriptions of the utterance. The ASR module maythen come up with sequences of words, e.g., tokens, based on certainconstraints. Other modules, such as a natural language understanding(“NLU”) module, may then interpret the user's words based on output fromthe ASR module to determine some actionable intent from the user'sutterance.

An ASR module may utilize various models to recognize speech, such as anacoustic model and a language model. The acoustic model is used onfeatures of audio data to generate hypotheses regarding which words orsubword units (e.g., phonemes) correspond to an utterance based on theacoustic features of the utterance. The language model is used todetermine the most likely transcription of the utterance based on thehypotheses generated using the acoustic model and lexical features ofthe language in which the utterance is spoken. In a commonimplementation, the ASR module may employ a decoding graph whenprocessing a given utterance into a sequence of word tokens allowed bythe underlying language model.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of various inventive features will now be described withreference to the following drawings. Throughout the drawings, referencenumbers may be re-used to indicate correspondence between referencedelements. The drawings are provided to illustrate example embodimentsdescribed herein and are not intended to limit the scope of thedisclosure.

FIG. 1 is a block diagram of an illustrative spoken language processingsystem, showing interactions between an automatic speech recognitionmodule and a client device user interface during and following theprocessing of an utterance.

FIG. 2 is a flow diagram of an illustrative process for processing anutterance and presenting a client with one or more potential intentresults along with corresponding content slot values.

FIG. 3 is a block diagram of an illustrative automatic speechrecognition module configured to provide content richness for certaincontent slots.

FIG. 4 is a block diagram of an illustrative automatic speechrecognition module utilizing a decoding graph containing tagsidentifying one or more particular intents along with carrier phrasesand content slots.

FIG. 5 is a flow diagram of an illustrative automatic speech recognitionmodule that may process an utterance using a single decoding graphcontaining multiple possible intents.

DETAILED DESCRIPTION Introduction

Speech processing may include the following steps: audio is receivedfrom a user, speech recognition is performed to obtain the text of theuser's speech, natural language understanding is performed to obtain asemantic representation, and some action is performed in response. Forexample, a user may say, “What is the weather in Boston?” This speechmay be converted to text, the text may be converted to a semanticrepresentation comprising an “obtain current weather” intent with a slotindicating the weather forecast for Boston is sought, and a response maybe generated for the user. Generally, the step of performing naturallanguage understanding is performed separately and after speechrecognition has been performed. Generally described, aspects of thepresent disclosure relate to combining portions of natural languageunderstanding together with speech recognition so that they areperformed together and not separately, as well as efficientlyrepresenting the results of the speech processing to a user.

Natural language understanding relates to determining a semanticrepresentation of a user input. Natural language understanding generallyoperates on text. The text may be received from a user or the text canbe transcribed from a user's speech. In some implementations, a semanticrepresentation may comprise an “intent” and one or more “slots.” Userintents may be classified into different categories. For example, userintents may include “purchase item/shopping list,” “play music,” “pausemusic,” “obtain current weather,” and “send email.” Each of theseintents may be only part of a semantic representation. “Slots” may beused to provide additional information. Each intent may be associatedwith one or more different slots. For example, the “purchaseitem/shopping list” intent may be associated with an “item” slot. When auser says “buy milk,” the NLU module may determine that this correspondsto the “purchase item” intent with the slot “item” having a value of“milk.”

Additional aspects of the present disclosure relate to presentation of auser interface (“UI”) for obtaining confirmation of or corrections to asemantic representation generated by an ASR and/or NLU modules. In oneembodiment, after processing a given utterance, the ASR module maydeliver ASR results with one or more intents, ranked by intentlikelihood or some other correlate of correctness, to the NLU module.The ASR module may produce multiple transcriptions for a single userutterance. For example, if a user says, “Play Thrift Shop byMacklemore,” the ASR results may include the correct transcription aswell as alternative transcriptions corresponding to alternative intents,such as the request, “Please shop for macaroni,” or the request, “Pleaseshop for mascarpone.” An NLU module may thus process more than onetranscription and return multiple semantic representations. For example,the above three results may correspond to a “play music” intent with asong slot of “thrift shop” and an artist slot of “macklemore”; a“purchase item” intent with an item slot of “macaroni”; and a “purchaseitem/shopping list” intent with an item slot of “mascarpone.”

Conventional speech processing systems may display alternative speechrecognition results and allow a user to select a correct result. Bypresenting a greater number of alternative results, it is more likelythat the correct result will be included. Presenting too many results,however, may be confusing or too laborious to provide a good userexperience. The present disclosure also relates to presenting resultsthat are sorted or organized by the underlying intent to make it easierfor a user to select the correct result. For example, the UI may presenta list of the top-N intents that were contained in the NLU results, suchas “play music” intent and “purchase item” intent. The UI may show theuser each likely intent result coupled with the top choice word or wordsfor the content slot portions. If one of the intents is the correctintent and the corresponding content slot portion is also correct, theuser may approve that intent and the action will initiate. However, ifone of the displayed intents is the correct intent, but the content slotportion is incorrect, the user may instead select the content portioncorresponding to the correct intent. At this point, the applicationknows which specific intent the user desires and the UI may insteaddisplay a larger choice list for the content slot that is appropriatelyfocused on the desired intent. For example, if the UI displays both ashopping list intent with a content slot entry of “dinner rolls” and aplay music intent with a content slot entry of “Rolling in the Deep,”but the user actually wished to hear “Proud Mary (Rolling on theRiver),” the user may select the content slot corresponding to the songtitle of the play music intent. The UI may then display a listcontaining the top choices for the song title content slot portion ofthe play music intent. In an alternate embodiment, the user may bepresented with the top result for the intent and the correspondingcontent slot. The user may select either the intent or the content slotto display additional choices for the intent or content slot portions ofthe semantic representation.

Some aspects of the disclosure relate to combining aspects of NLUprocessing with ASR processing. Generally, ASR results include text, forexample, in the format of a lattice or an N-best list, and the ASRresults do not include information about a semantic representation ofthe text. In some aspects, a decoding graph for ASR (such as a finitestate transducer) may be augmented with information about the underlyingsemantic meaning of the text. For example, portions of the decodinggraph may be associated with information (e.g., metadata) indicating anintent or a slot that corresponds to the text. This association may beindicated by “tagging” the portion with the associated metadata. A tagassociated with a subsequent path (e.g., a word) of the decoding graphmay indicate whether the arc path corresponds to the carrier phraseportions of the intent, the content slot portions of the intent, etc. Aword or phrase may be tagged as part of a specific intent, such as theplay music intent, but may also be identified as part of the carrierphrase language for that intent. Other tags may indicate that a givenword or word sequence is part of a content slot (e.g., a highlyinformative, intent-specific portion) for that specific intent. Eachword may be tagged for multiple intents with additional tagscorresponding to intent-specific information. These tags allow a resultsgenerator or some other component of an ASR module to generate richnessinto the results that it outputs to the NLU module by generating aplurality of different word tokens corresponding to individual contentslots of particular intents. As the ASR results (e.g., an N-best list orlattice) that are produced using the decoding graph may also includeinformation about the associated intents and slots, semanticrepresentations may be derived during ASR and separate NLU processingmay not be necessary.

Although aspects of the embodiments described in the disclosure willoften focus, for the purpose of illustration, on a spoken languageprocessing system processing user utterances related to playing music,one skilled in the art will appreciate that the techniques describedherein may be applied to any subject or domain of spoken languageprocessing. For example, a system implementing the features describedherein may process user utterances related to phone dialing, shopping,getting directions, playing music, performing a search, and the like. Inaddition, aspects of the embodiments described herein focus, for thepurpose of illustration, on a client device that transmits data to anetworked spoken language processing system for processing. However, thesystems and techniques described herein may be implemented on a singledevice, such as a user device that both receives spoken input andprocesses the input to determine the user's intent. Various aspects ofthe disclosure will now be described with regard to certain examples andembodiments, which are intended to illustrate but not limit thedisclosure.

Speech Recognition Environment

Prior to describing embodiments of processes for analyzing utterancesusing an intent-aware ASR module, an example environment in which theprocesses may be implemented will be described. FIG. 1 depicts a spokenlanguage processing system 100 showing interactions between a spokenlanguage processing system 100 and a client device 130 during theprocessing of an utterance as well as the visible manifestation of theseinteractions. The spoken language processing system 100 illustrated inFIG. 1 can be a network-accessible system in communication with theclient device 130 via a communication network 140, such as a cellulartelephone network or the Internet. A user 120 may use the client device130 to submit utterances, receive information, and initiate variousprocesses, either on the client device 300 or the spoken languageprocessing system 100. For example, the user 120 can issue spokencommands to the client device 130 in order to get directions or listento music, as described above.

The client device 130 can correspond to a wide variety of electronicdevices. In some embodiments, the client device 130 may be a mobiledevice that includes one or more processors and a memory which maycontain software applications executed by the processors. The clientdevice 130 may include a speaker or other audio output component forpresenting or facilitating presentation of audio content. In addition,the client device 130 may contain a microphone or other audio inputcomponent for accepting speech input on which to perform speechrecognition. Illustratively, the client device 130 may be any computingdevice such as a wireless mobile device (e.g. smart phone, PDA, tablet,or the like), wearable device (e.g., “smart” watch or “smart” eyewear),desktop computer, laptop computer, media player, video game console,electronic book reader, television set-top box, television (e.g.,“smart” TV), or computerized appliance, to name a few. The software ofthe client device 130 may include components for establishingcommunications over wireless communication networks or directly withother computing devices.

The spoken language processing system 100 can be any computing systemthat is configured to communicate via a communication network. Forexample, the spoken language processing system 100 may include anynumber of server computing devices, desktop computing devices, mainframecomputers, and the like. In some embodiments, the spoken languageprocessing system 100 can include several devices physically orlogically grouped together, such as an application server computingdevice configured to perform speech recognition on an utterance and adatabase server computing device configured to store records and speechrecognition models.

The spoken language processing system 100 can include an ASR module 102,an NLU module 106, data store 110, and one or more applications 108. Insome embodiments, the spoken language processing system 100 can includevarious modules and components combined on a single device, multipleinstances of a single module or component, etc. For example, the spokenlanguage processing system 100 may include a separate database serverthat may be configured with a data store 110; a server or group ofservers configured with both ASR and NLU modules 102 and 106; and aserver or group of servers configured with one or more applications 108.The ASR module 102 may consist of a variety of models and contain adecoding graph with tags 103 and a results generator 104 that generatesresults before sending them to the NLU module 106. In multi-deviceimplementations, the various devices of the spoken language processingsystem 100 may communicate via an internal communication network, suchas a corporate or university network configured as a local area network(“LAN”) or a wide area network (“WAN”). In some cases, the devices ofthe spoken language processing system 100 may communicate over anexternal network, such as the Internet, or a combination of internal andexternal networks.

In some embodiments, the features and services provided by the spokenlanguage processing system 100 may be implemented as web servicesconsumable via a communication network 140. In further embodiments, thespoken language processing system 100 is provided by one or more virtualmachines implemented in a hosted computing environment. The hostedcomputing environment may include one or more rapidly provisioned andreleased computing resources, which computing resources may includecomputing, networking and/or storage devices. A hosted computingenvironment may also be referred to as a cloud computing environment.

The network 140 may be a publicly accessible network of linked networks,possibly operated by various distinct parties, such as the Internet. Inother embodiments, the network 140 may include a private network,personal area network (“PAN”), LAN, WAN, cable network, satellitenetwork, etc. or some combination thereof, each with access to and/orfrom the Internet. For example, the devices of the spoken languageprocessing system 100 may be located within a single data center, andmay communicate via a private network as described above. The clientdevice 130 may communicate with spoken language processing system 100via the Internet. The client device 130 may have access to the Internetvia a wired or Wi-Fi connection, or via a cellular telephone network(e.g., a Long Term Evolution or LTE network).

In one embodiment, the client device 130 may receive the most likelyresults responsive to an utterance processed by the spoken languageprocessing system 100. The results may be ranked by intent likelihood orsome other correlate of correctness. The client device 130 may presentthe user 120 with a client device UI 150 a detailing the most likelyintent 152 or intents, 152 and 156, as one or more semanticrepresentations. The semantic representations may indicate a possibleaction desired by a user, and correspond to, among others, intents,content slots, and carrier phrases. The carrier phrases may benormalized and correspond to one or more intents. The UI 150 a may alsodetail the top content slot predictions 154 and 158 for one or morecontent slots of the most likely intent(s) 152 and 156. In otherembodiments, the user 120 may be presented with a client device UI 150 adetailing additional intents, e.g., more than two. The content slots areassociated with highly informative, intent-specific portions of a userutterance. They can be used by an NLU module 106, application 108, orsome other module or component of the spoken language processing system100 to “hold” the high-information semantic elements regarding what theuser 120 has said. For a given intent, there may be several possiblechoices of words as values for the content slot portions of that intent.The user 120 may approve the correct intent 152, which may be presentedto the user by a carrier phrase corresponding to the intent, triggeringthe client device 130 to perform the desired action. For example, theuser 120 may approve a play music intent 152, with a top choice of“Rolling in the Deep” 154 as the value for the song title content slotof the intent. An application 108 may then cause the client device 130to begin playing the selected song.

The user 120 may also select the correct intent 152 from the given list,but the corresponding top content slot prediction 154 may not correspondto the utterance of the user 120. The client device 130 may then presentthe user 120 with a UI 150 b detailing a plurality of different wordtokens 162 demonstrating a rich selection of options for the contentslot, each option corresponding to the correct intent. Returning to theexample above, the user 120 may have approved the play music intent 152but the top content slot value of “Rolling in the Deep” 154 for the playmusic intent 152 may have been inaccurate. The user may use the UI 150 b(or some other UI providing similar functionality) to select from anextended list of top values 162 corresponding to the chosen intent 152.Potential top values that do not correspond to the chosen intent 152 maynot be shown. Illustratively, the user may select the correct contentslot value of “Proud Mary (Rolling on the River).” In some embodiments,the client device 130 may also present the user 120 with tokens 162 and164 corresponding to multiple (e.g., two, three or more) intents 152 and156. The user 120 may select the correct content slot value from theavailable values 162 and 164 corresponding to intent 152 and 164,respectively.

Sample Process for Presenting UIs with Intent-Specific ASR Results

FIG. 2 illustrates a sample process 200 for processing a user utteranceusing an intent-aware ASR module. The process 200 can includepresentation of a client device UI 150 depicting one or more intentresults with choices for one or more content slots.

The process 200 begins at block 202. The process 200 may beginautomatically upon initiation of a speech recognition session. Theprocess 200 may be embodied in a set of executable program instructionsstored on a computer-readable medium, such as one or more disk drives,of a computing system of the spoken language processing system 100. Whenthe process 200 is initiated, the executable program instructions can beloaded into memory, such as RAM, and executed by one or more processorsof the computing system.

At block 204, the spoken language processing system 100 can receive anutterance from a client device 130. As described above, the userutterance may be a spoken command to play a recorded music file. Forexample, the user 120 may say, “Play me ‘Rolling on the River.’” In someembodiments, the spoken language processing system 100 may not knowahead of time which intent the user 120 plans to target. The utterancemay be transmitted as live streaming audio.

At block 206, the spoken language processing system 100 can performspeech recognition processing on the utterance. An ASR module 102 mayutilize various models (e.g., language models, acoustic models) whendetermining the content of an utterance. The output of the ASR module102 may be a lattice or N-best list of likely transcriptions of the userutterance. In some embodiments, rather than processing the utteranceinto textual transcriptions, the ASR module may process the utteranceinto one or more phonemic transcriptions (e.g., the lattice or N-bestlist contains the utterance transcribed into a sequence of phonemesrather than text). As described in greater detail below, the ASR modulemay use a decoding graph with tags or information corresponding towords, intents, content slots, and the like in order to produce resultsrich with appropriate recognition options for the content slots ofvarious intents. In some embodiments, the ASR module 102 may utilizemodels specialized for a single intent or domain when the ASR module 102knows the domain to which the utterance relates. Such specialized modelscan improve the efficiency and accuracy of the ASR module 102. In someembodiments, the ASR module 102 may utilize multiple single-domainmodels in parallel. This implementation may provide improved efficiencywhen used with, e.g., multi-core processors or when ASR processing isdistributed among multiple computing devices.

At block 208, the NLU module 106 may receive the intent-specific resultsfrom the ASR module 102, such as a lattice of likely transcriptions. TheNLU module 106 can identify the likely user intent based on theseintent-specific results. The NLU module 106 may query a data store 110and verify the accuracy of the named entity values. For example, the NLUmodule 106 can choose the highest scoring artist/song pair prior toproviding the output to application 108 at block 210. One example of anNLU system that provides such features is disclosed in U.S. patentapplication Ser. No. 13/786,237, entitled “NAMED ENTITY RESOLUTION INSPOKEN LANGUAGE PROCESSING” and filed on Mar. 5, 2013, which is herebyincorporated by reference in its entirety for all that it discloses. Theapplication 108 may process the results and send them to the clientdevice 130. If multiple intents are delivered by the NLU module 106 tothe application 108, the application 108 may generate or otherwise causepresentation of a client device UI 150 a to the user 120 to show that ithas identified multiple (e.g., two or more) intents in response to theutterance.

At block 212, the client device 130 can present the user 120 with a UI150 a depicting each intent result 152 and 156 with a top choice (e.g.,a most-likely or highest-scoring option) for each intent result'scontent slot(s). If one of the intent results, along with itscorresponding top choice for the content slot or slots, is correct, theuser 120 may approve at block 218. However, if the user interface doesnot show the correct entry as a choice for one or more of the intentresult's content slots, the user 120 may instead select the content slotof the correct intent result at block 214. In one embodiment, the user120 may indicate in some manner which intent is correct. For example,the user 120 may hover over the correct intent, click or tap the intent,use a touch-screen gesture, use spoken commands to select the intent,look at the intent if the device is configured with eye-movementtracking, etc. The application 108 then knows what the correct intent isand may provide the user 120 with additional choices specific to thatintent. The client device UI 150 b may present the user 120 with a listof top content slot choices, e.g., list 162, at block 216. The user 120may then approve of the correct content slot choice at block 220.

Once the user 120 approves of both the intent and the content slot orslots, either at block 218 or block 220, the application 108 may performthe intended task of the user 120 at block 222. For example, theapplication 108 may play “Proud Mary (Rolling on the River)” utilizingthe client device 130 as per the initial utterance of the user 120. Theprocess ends at block 224.

Sample Intent-Aware ASR Module

In some embodiments, the spoken language processing system 102 mayinclude an ASR module 102 configured to provide intent-specific resultsfor various domains. FIG. 3 is a conceptual illustration of one exampleof an ASR module 102 that acts as an intent-aware speech recognizer formultiple (e.g., two or more) domains. As shown, the ASR module 102 maycontain a decoding graph 103 and a results generator 104. The decodinggraph 103 describes the sequences of words that are possible and mayconsist of, be linked to, or otherwise be associated with a variety ofdifferent models. These models may include an acoustic model, grammarmodel, statistical language model, etc. The decoding graph 103 can beused to recognize utterances in any number of domains 130, and eachdomain may include one or more intents, such as intent 1 300 and intentN 310. The results generator 104 may determine which results to provideas output based on the processing done with the decoding graph 103, asdescribed in greater detail below.

The decoding graph 103 or portions thereof may have “tags” correspondingto various types of information such as tokens, including words,intents, and values for content and carrier phrases. As described above,a semantic representation may include an intent and one or more slots. Aparticular intent may be associated with one or more low-informationcarrier phrase portions 302. The semantic representation may alsoinclude one or more high-information content slots 304. Values for thesecontent slots may correspond to the particular intent of the semanticrepresentation. In some embodiments, one word or word sequence in thedecoding graph 103 may be tagged with metadata, such as an identifier ordescriptor, indicating that the word or word sequence corresponds to aparticular intent 300, such as the play music intent. The word may alsobe tagged with additional information. This additional information mayinclude metadata indicating that the word is part of the carrier phrase302 for the intent 300, or a value for the content slot 304, anidentifier or descriptor of a particular content slot 304, etc.

Generally described, an arc path or some sequence or set of nodes in thedecoding graph may correspond to one or more semantic representations.The semantic representation may include an intent, such as one of theillustrated intents 300 and 310. The arc for a path in the decodinggraph 103 that corresponds to a particular semantic representation maybe associated with tags including information relevant to that semanticrepresentation's intent, such as an identifier or descriptor of theintent (e.g., “intent 1” or “play music”). Each portion of the graphthat is specific to a given semantic representation can also have tagsidentifying values for carrier phrase portions 302 of the intent 300,values for content slot portions 304 of the semantic representationcorresponding to the intent 300, etc. A word may be tagged as belongingto the carrier phrase 302 for a given intent 300 but may also correspondto a value for the content slot 304 for another semanticrepresentation's intent 310. For example, when the semanticrepresentation comprises the play music intent, the word “shop” may betagged as a value for a song title content slot (e.g., “slot 1” or “songtitle”) because the musical artist Macklemore has a song named “ThriftShop.” However, for a semantic representation comprising the shoppinglist intent, “shop” may simply correspond to a carrier phrase portionand will be tagged as such.

A results generator 104 that produces ASR results based on paths takenthrough the decoding graph 103 can use the tags to identify carrierphrase portions and content slots in the results. In some embodiments,the results provided by the results generator 104 for carrier phraseportions may be highly constrained, while a rich set of options may beprovided for content slots. For example, when generating an N-best list,lattice, or other type of results, the results generator 104 can addalternatives for content slot portions of a semantic representation andreduce the number of options for the carrier phrase portions of thesemantic representation's intent. As described above and in greaterdetail below, such ASR results can provide a rich set of options for thehigh-information content slot portions that can be used by an NLUmodule, application, or some other downstream process. In addition, theresults may be constrained or normalized in the carrier phrase portionsbecause different options for the carrier phrase portions may notmaterially alter the meaning of the results or the recognition of theuser utterance.

When processing an utterance using the decoding graph 103, history maybe recorded regarding which paths and/or nodes were being considered forparticular portions of the utterance. The tags for paths or nodes of thedeciding graph 103 can appear in ASR decoding history as metadata, belinked to from the decoding history, or otherwise be accessible from ordetermined by information in the decoding history. In this way, the ASRdecoding history allows the results generator 104 to identify the intentto which a particular word sequence that has been decoded likelybelongs, along with locations of carrier phrase portions and contentslot portions in the sequence. When building results based on thehistory for a particular utterance, the results generator 104 can focuson providing multiple intent-specific, content-slot-specific, orotherwise context-appropriate results for the tagged content slotportions. Thus, for each semantic representation in the resultsgenerated by the results generator 104, a plurality of different wordtokens may be provided for a portion of the utterance that correspondsto the content slot for the given semantic representation's intent. Theresults may be ranked by intent likelihood or some other correlate ofcorrectness. These results may be in the form of a lattice or N-bestlist of likely transcriptions of the user utterance. The same processmay not be used for carrier phrase portions, however. For example, theresults generated by the results generator 104 may include somecanonical text for the carrier phrase portions.

In some embodiments, the spoken language processing system 102 mayinclude multiple single-domain ASR modules. Each ASR module cancorrespond to a single domain or intent. For example, one ASR module maycorrespond to a “music domain” (including intents such as a play musicintent), one ASR module may correspond to a weather domain, one maycorrespond to a travel domain, and another may correspond to a shoppingdomain. Each module may include its own decoding graph with tagscorresponding to the carrier phrase and content slot portions of intentsfor the specific domain. For example, in a single-domain ASR moduledecoding graph corresponding to the play music intent, the word “shop”may only be tagged as corresponding to the “song title” content slot.

FIG. 4 depicts an illustrative decoding graph 103. The decoding graph103 includes paths corresponding to various semantic representations,each of which may comprise an intent and one or more slots. The graphbegins at path segment 400. Path segment 400 comprises two differentarcs, one corresponding to “please” and one corresponding to “play.”Both may be carrier phrases. For example, “please” may be a globalcarrier phrase corresponding to any intent, while “play” may be acarrier phrase corresponding to the play music intent.

The decoding graph 103 may be a finite state transducer. A finite statetransducer is a graph that may include all possible utterances that maybe recognized by an ASR engine. A finite state transducer may be staticin that it is created before speech recognition begins and the samefinite state transducer may be used for the recognition of allutterances. Multiple finite state transducers may also be dynamicallylinked together during the course of speech recognition, in order tocreate a larger finite state transducer. This larger finite statetransducer may be customized, e.g., for a particular speaker or subjectmatter.

Path segment 402 comprises four different arcs: “hip,” “hop,” “thrift,”and “shop.” These arcs may be associated with metadata. In one example,the metadata may consist of tags. The metadata may correspond to, forexample, a content slot portion for a particular semanticrepresentation, such as a “song title” content slot of the “play music”intent. Metadata corresponding to song titles may include the wordsand/or tokens “hip,” “hop,” “thrift,” and “shop.” However, the metadatamay also correspond to carrier phrases. For example, the word and/ortoken “shop” may also be associated with metadata corresponding to acarrier phrase portion of the shopping intent. Path return <epsilon>,e.g., segment 404, indicates that the results generator 104 mayrepeatedly follow arc path segments corresponding to content slots inorder to generate a plurality of words and/or tokens corresponding tothese content slots. Different paths may follow different arc pathsegments.

The path may include additional arcs associated with different metadata.For example, another path segment 406 may correspond to a carrier phraseportion of the given intent and may be tagged as such. Metadatacorresponding to a carrier phrase portion of the play music intent mayinclude, for example, the words and/or tokens “by” and “for.”

Path segment 408 comprises six different arcs: “wanz,” “weezer,”“macklemore,” “macaroni,” “mascarpone,” and “cheese.” These arcs mayalso be associated with metadata. The metadata may correspond to, forexample, an additional content slot portion of the semanticrepresentation, such as an “artist” content slot for a semanticrepresentation comprising the “play music” intent. Metadatacorresponding to artists may include the words and/or tokens “wanz,”“weezer,” and “macklemore.” However, the metadata may also correspond tocontent slot portions of other semantic representations comprisingdifferent intents. For example, the word and/or token “macaroni,”“marscarpone,” and “cheese” may be associated with metadatacorresponding to a value for the content slot portion of the shoppingintent. Path return <epsilon>, e.g., segment 410, indicates that theresults generator 104 may repeatedly follow arc path segmentscorresponding to content slots in order to generate a plurality of wordsand/or tokens as values corresponding to these content slots. Forexample, the results generator 104 may follow the decoding graph togenerate results corresponding to the utterance, “Play ‘Thrift Shop’ byMacklemore.” One possible path may consist of arcs “play”-“thrift[shop]”-“by”-“macklemore” The results generator may generate results forcontent slots corresponding to the play music intent, such as “hip,”“hop,” or “thrift,” and “shop.” The results generator 104 may determinethat another likely intent is the shopping list intent and follow thepath “please”-“shop”-“for”-“macaroni.” These examples are included forillustrative purposes only as there may be many different ways torepresent the idea that a token or word may correspond to multipleintents and be tagged with different metadata in each intent.

When the results generator 104 generates output for delivery to the NLUmodule 106 (or some other downstream module or process), such as anN-best list, it may determine, based on the encountered metadata, that acertain portion of the graph corresponds to a given semanticrepresentation comprising an intent and a given portion of that semanticrepresentation. For example, the results generator 104 may encounter aword that, based on metadata for the path segment 402 that correspondsto the position of the word in the utterance, is likely is part of asong title (e.g., “hip”). The results generator 104 may also know, basedon metadata, that the word corresponds to the play music intent.

The results generator 104 may look at hypotheses or words that it wasexamining for some portion of the utterance, e.g., at path segment 408,and filter those words to only the “play music” intent words, or to onlywords tagged as “artist” (e.g., “macklemore,” “wanz,” and “weezer”) inorder to generate a plurality of tokens for that content slot. In thisway, the results generator 104 can produce a list with the most likelyresults for a given semantic representation's intent. If the resultsgenerator 104 did not separate, filter, or choose words by intent, theresults generator 104 would produce a list with very similar words, asthe tokens for each intent would be all mixed together. For example, theresults generator 104 would produce results corresponding to a semanticrepresentation with the play music intent with words and/or tokens suchas “macklemore,” “macaroni,” and “mascarpone.” However, some of thesewords would correspond to different intents, such as the shopping listintent, and thus may actually be less likely to be correct than a more“dissimilar” sounding word corresponding to the correct intent for thesemantic representation.

Speech recognition may be performed on some audio data, which mayinclude an utterance, using a decoding graph 103 such as the onedescribed above. The speech recognition results may include multipletranscriptions of the audio data. Each transcription may be a semanticrepresentation associated with a different intent or may containdifferent values for the content slots of each utterance. Tokens,including words, within the transcriptions may correspond to differentintents, content portions, or carrier portions.

In one intent-specific, non-limiting embodiment, scores (e.g.,likelihoods, probabilities, or confidence scores) may be calculated forwords. The scores may indicate the likelihood that a word is thecorrectly recognized word for a given portion of the utterance. Bydetermining result options based on tags as described above (e.g.,generating separate results for different intents or content slots), afirst word with a lower score than a second word may be included in theresults while the second word or phrase may be excluded from the results(or the first word may be otherwise elevated over the second word). Thiscan be done based on a determination that the first word is part of, orassociated with, values for the intent or content slot of the particularportion of the results that is currently being generated, while thesecond word is not. The second word may still be included in the resultsas a lower-ranked option, or in a portion of the results that isassociated with a different intent. Returning to the example UI 150 ashown in FIG. 1, the phrase “dinner rolls” (or individual words in thephrase) may be associated with a higher score than the phrase “Rollingin the Deep” (or individual words in the phrase). However, for theportion of the results associated with the “play music intent,” thephrase “dinner rolls” has been excluded as a possible value. Additionalresults have also been generated, at least one of which corresponds to a“shopping list” intent. The phrase “dinner rolls” has been included as avalue for that portion of the results.

FIG. 5 depicts a flow diagram of an illustrative ASR process 500 using asingle decoding graph containing multiple possible intents. The process500 begins at block 502. The process 500 may be viewed as a subset ofblock 206 in FIG. 2. The process 500 may be embodied in a set ofexecutable program instructions stored on a computer-readable medium,such as one or more disk drives, of a computer device associated withthe spoken language processing system 100. When the process 500 isinitiated, the executable program instructions can be loaded intomemory, such as RAM, and executed by one or more processors of thecomputing system.

At block 504, an utterance is received by the ASR module 102. Asdescribed above, the user utterance may be a spoken command to play arecorded music file. For example, the user 120 may say, “Play ‘Rollingon the River’ by Tina Turner.” The ASR module 102 may use a singledecoding graph 103 that covers several different intents that the user120 may express through a given utterance.

At block 506, the ASR module 102 can process a given utterance from theuser 120. As described in detail above, the decoding graph 103 mayinclude tags that can be saved in the decoding history during ASRprocessing. For example, as the ASR module 102 traverses the decodinggraph 103, determining whether a portion of the utterance likelycorresponds to each arc in a particular path, tags may be encountered.In addition to storing a record of the arcs visited, scores calculated,and other such information, the tags (or some link to the arcs or tags)may be stored for use by the results generator 104, as described below.

The results generator 104 may build results using tags in the decodinghistory. The results generator 104 may use an algorithm for creating alist of results. The results may be provided as an N-best list, aconfusion network, etc. The results generator 104 may include variousresults for both the carrier phrase portions and the content slotportions, but may focus on generating a rich set of results for thecontent slot portions.

The results generator 104 or some other module or component may, atblock 508, identify the tags (e.g., in the decoding history) from eachportion of the decoding graph (or some subset thereof) that was used torecognize the current utterance. The tags may identify the intent, thecarrier phrase portions of the intent, and the content slot portions ofthe intent in order to generate various semantic representations. Forexample, the most likely intents may be the play music intent at node412 followed by a shopping list intent at node 402. For the aboveutterance, the “play (the song)” portion or element may be tagged as alow-information or carrier phrase element, as it simply indicates whatintent the user wishes to trigger, e.g., the play music intent. The songtitle and the artist name may be tagged as content slot portions of theintent. In the play music intent example, the play music intent may havea particular number of slots that need filling, e.g., artist, album, andsong.

At block 510, the results generator 104 or some other component may usethe tags in the decoding graph to collapse the carrier phrase componentsof a given intent (e.g., reduce the number of different words,associated with the same intent, that have been recognized for the sameportion of the utterance). The results may be constrained in the carrierphrase portions because different options for the carrier phraseportions may not materially alter the meaning of the results. In manycases, the carrier phrase components for a given intent may or may notcorrespond to carrier phrase portions for another intent. In addition,an element tagged as a carrier component for some intent (e.g., theshopping list intent) may also be tagged as part of the content slotportion of another intent (e.g., the play music intent). Furthermore, anelement tagged for a content slot of one intent may also be tagged asfor the same or a different content slot of a different intent.

In the present example, the results generator 104 may collapse thepotential results corresponding to the carrier phrase component of theshopping list intent but may generate a rich set of results for thecontent slot portion of the play music intent. For example, the word“shop” may be part of a carrier phrase component of the shopping listintent, but part of the content slot portion of the play music intent asit corresponds to a song title.

At block 512, the results generator 104 or some other component canproduce a plurality of tokens for the content slots corresponding to agiven intent. When the results generator produces results for more thanone intent, it may produce a rich list of options for content slots ofthe most likely intent as well as provide richness for content slots ofother intents correlating to likely choices. For example, the resultsgenerator 104 may filter words that were examined for a portion of theutterance to only those words corresponding to a likely intent, or towords tagged as corresponding to content slot portions for a givenintent.

At block 514, the result generator 104 or some other component candeliver its results to the NLU module having ranked the results by theevent likelihood. These results may be in the form of a lattice orN-best list of likely transcriptions of the user utterance. The resultsmay be organized by intent. The results may comprise ranking intents.Such ASR results may provide a rich set of options for high-informationcontent slots that can be used by the NLU module, an application, orsome other downstream process. The process ends at block 516.

In another embodiment, a single-domain ASR module contains a singledecoding graph corresponding to a single possible intent. The decodinggraph may contain tags identifying, among other things, carrier phraseand content portions of the given intent. A results generator canproduce a plurality of tokens for the content slots corresponding to agiven intent while collapsing the carrier portions of the given intent.The results generator can produce results from numerous single-domainASR modules that simultaneously (or substantially simultaneously)processed the same utterance, each for a different intent. The resultsgenerator can then deliver a top list of results, as an N-best list, aconfusion network, etc., to the NLU module. Speech recognition resultsfrom the spoken language processing system 100 may include multipletranscriptions of the audio data. Each transcription may be associatedwith a different intent or may contain different values for the contentslots of each utterance. The results may include a transcript or n-bestlist of transcripts for a portion of the audio data, a cumulativetranscript or n-best list of transcripts, part of a lattice, part of aconsensus network, any other kind of speech recognition result known tothose of skill in the art, etc. The results may only includetranscriptions associated with a specific intent.

Additional Embodiments

This invention also includes methods for taking advantage of multiplecentral processing units (“CPUs”) in parallel in order to reduce latencydue to processing times. This more focused training may improve accuracyboth for users as a whole as well as for individual users. The ASRmodule may implement cross-intent prevention during the decoding bycarrying intent tags forward and utilizing the tags to make decisionsabout which decoder arcs to explore. The invention may also use separateCPU resources, or threads, for each intent being processed, whilesharing a decoding graph as well as the acoustic scoring across allthreads. The invention may examine what intents are most likely tocorrespond to a user's utterance in order to determine which intents ordomains are assigned to different threads working in parallel.

The invention may allow for improvements to the acoustic models used forASR, the language model used for ASR, the NLU intent-classifier models,the NLU entity-resolution models, the user-interface models coveringuser-interface behavior that may be automatically learned, and othermodels used for ASR, NLU, and user-interface that are known to those ofskill in the art. The invention also allows for more focuseddiscriminative training, both for a global static speech model and foruser-specific speech models. The invention may allow for improvementswithin intent modeling as there may be less focus on carrier phraseregions and more focus on content slot regions. The invention may allowfor improvements in cross-intent discrimination. For cross-intentdiscrimination, there may be more focus on carrier phrase regions. Theinvention may also aid in learning cross-pruning thresholds.Additionally, the invention may be used to improve other features in aglobal model, such as adjusting results based on the time of day orbased on trending social data. Locally, the invention may use statisticsregarding user results and implicit or explicit feedback regarding modelsuccess, user browsing habits, movie viewing, etc. The invention mayalso allow for short-term updates and model refinements based on trendsobserved in other media.

The invention may also use intent-specific richness to discriminativelyadapt the static speech model to improve the likelihood of correctintents compared to lower-likelihood intents. The invention may furtherutilize the intent-specific richness to discriminatively adapt theuser-specific parts of the speech model and the decoding graph. The useof intent-specific ASR modules may be complimentary to a multi-domainNLU module, allowing the NLU module to focus on using models based onspecific intents.

Terminology

Depending on the embodiment, certain acts, events, or functions of anyof the processes or algorithms described herein can be performed in adifferent sequence, can be added, merged, or left out altogether (e.g.,not all described operations or events are necessary for the practice ofthe algorithm). Moreover, in certain embodiments, operations or eventscan be performed concurrently, e.g., through multi-threaded processing,interrupt processing, or multiple processors or processor cores or onother parallel architectures, rather than sequentially.

The various illustrative logical blocks, modules, routines, andalgorithm steps described in connection with the embodiments disclosedherein can be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. The described functionality can beimplemented in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the disclosure.

Moreover, the various illustrative logical blocks and modules describedin connection with the embodiments disclosed herein can be implementedor performed by a machine, such as a general purpose processor device, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or otherprogrammable logic device, discrete gate or transistor logic, discretehardware components, or any combination thereof designed to perform thefunctions described herein. A general purpose processor device can be amicroprocessor, but in the alternative, the processor device can be acontroller, microcontroller, or state machine, combinations of the same,or the like. A processor device can include electrical circuitryconfigured to process computer-executable instructions. In anotherembodiment, a processor device includes an FPGA or other programmabledevice that performs logic operations without processingcomputer-executable instructions. A processor device can also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Although described herein primarily with respect todigital technology, a processor device may also include primarily analogcomponents. For example, some or all of the signal processing algorithmsdescribed herein may be implemented in analog circuitry or mixed analogand digital circuitry. A computing environment can include any type ofcomputer system, including, but not limited to, a computer system basedon a microprocessor, a mainframe computer, a digital signal processor, aportable computing device, a device controller, or a computationalengine within an appliance, to name a few.

The elements of a method, process, routine, or algorithm described inconnection with the embodiments disclosed herein can be embodieddirectly in hardware, in a software module executed by a processordevice, or in a combination of the two. A software module can reside inRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory,registers, hard disk, a removable disk, a CD-ROM, or any other form of anon-transitory computer-readable storage medium. An exemplary storagemedium can be coupled to the processor device such that the processordevice can read information from, and write information to, the storagemedium. In the alternative, the storage medium can be integral to theprocessor device. The processor device and the storage medium can residein an ASIC. The ASIC can reside in a user terminal. In the alternative,the processor device and the storage medium can reside as discretecomponents in a user terminal.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without other input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z).Thus, such disjunctive language is not generally intended to, and shouldnot, imply that certain embodiments require at least one of X, at leastone of Y, or at least one of Z to each be present.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it can beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. As can berecognized, certain embodiments described herein can be embodied withina form that does not provide all of the features and benefits set forthherein, as some features can be used or practiced separately fromothers. The scope of certain embodiments disclosed herein is indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. One or more non-transitory computer readablemedia comprising executable code that, when executed, cause one or morecomputing devices to perform a process comprising: generating aplurality of semantic representations of audio data representing userspeech, the plurality of semantic representations generated using anatural language understanding model trained to classify input as one ormore intents, wherein the plurality of semantic representations comprisea first intent and a second intent, the first intent corresponds to afirst slot, the first slot corresponds to a first plurality of values,the second intent corresponds to a second slot, and the second slotcorresponds to a second plurality of values, and wherein the firstintent is associated with a score indicating a higher confidence in thefirst intent than the second intent; displaying a user interface thatpresents a first semantic representation comprising the first intent anda first value of the first plurality of values, wherein the userinterface is configured to: allow a user to select the second intent,wherein selection of the second intent causes execution of a secondfunction corresponding to the second intent instead of a first functioncorresponding to the first intent; and allow a user to select a secondvalue from a list, wherein the list includes at least a portion of thefirst plurality of values and does not include any values that are notincluded in the first plurality of values, and wherein selection of thesecond value causes execution of the first function corresponding to thefirst intent and the second value; receiving input representingselection of the second intent; executing the second function based atleast partly on the input representing selection of the second intent;and based at least partly on the input representing the selection of thesecond intent, modifying metadata for a token of the natural languageunderstanding model to indicate that the token is associated with thesecond intent, wherein the token corresponds to a portion of the userspeech.
 2. The one or more non-transitory computer readable media ofclaim 1, wherein the process further comprises displaying the firstplurality of values in an order corresponding to a likelihood that eachvalue corresponds to the user speech.
 3. The one or more non-transitorycomputer readable media of claim 1, wherein the process furthercomprises displaying a second semantic representation.
 4. The one ormore non-transitory computer readable media of claim 3, wherein theprocess further comprises displaying the first and second semanticrepresentations in an order corresponding to a likelihood that eachsemantic representation corresponds to the user speech.
 5. The one ormore non-transitory computer readable media of claim 1, wherein theprocess further comprises displaying the user interface that presentsthe first semantic representation by presenting a normalized carrierphrase corresponding to the first intent.
 6. A system comprising: acomputer-readable memory storing executable instructions; and one ormore processors in communication with the computer-readable memory,wherein the one or more processors are programmed by the executableinstructions to: generate a plurality of semantic representations ofaudio data representing user speech, the plurality of semanticrepresentations generated using a natural language understanding modeltrained to classify input as one or more intents, wherein the pluralityof semantic representations comprise a first intent and a second intent,the first intent corresponds to a first slot, the first slot correspondsto a first plurality of values, the second intent corresponds to asecond slot, and the second slot corresponds to a second plurality ofvalues, and wherein the first intent is associated with a scoreindicating a higher confidence in the first intent than the secondintent; display a user interface that presents a first semanticrepresentation comprising the first intent and a first value of thefirst plurality of values, wherein the user interface is configured to:allow a user to select the second intent, wherein selection of thesecond intent causes execution of a second function corresponding to thesecond intent instead of a first function corresponding to the firstintent; and allow a user to select a second value from a list, whereinthe list includes at least a portion of the first plurality of valuesand does not include any values that are not included in the firstplurality of values, and wherein selection of the second value causesexecution of the first function corresponding to the first intent andthe second value; receive input representing selection of the secondintent; execute the second function based at least partly on the inputrepresenting selection of the second intent; and based at least partlyon the input representing the selection of the second intent, modifymetadata for a token of the natural language understanding model toindicate that the token is associated with the second intent, whereinthe token corresponds to a portion of the user speech.
 7. The system ofclaim 6, wherein the user interface presents the first plurality ofvalues in an order corresponding to a likelihood that each value of thefirst plurality of values corresponds to the user speech.
 8. The systemof claim 6, wherein the user interface presents a second semanticrepresentation.
 9. The system of claim 8, wherein the user interfacepresents the first and second semantic representations in an ordercorresponding to a likelihood that each of the first semanticrepresentation and second semantic representation corresponds to theuser speech.
 10. The system of claim 6, wherein the user interfacepresents a normalized carrier phrase corresponding to the first intent.11. A computer-implemented method comprising: under control of one ormore computing devices configured with specific computer-executableinstructions, generating a plurality of semantic representations ofaudio data representing user speech, the plurality of semanticrepresentations generated using a natural language understanding modeltrained to classify input as one or more intents, wherein the pluralityof semantic representations comprise a first intent and a second intent,the first intent corresponds to a first slot, the first slot correspondsto a first plurality of values, the second intent corresponds to asecond slot, and the second slot corresponds to a second plurality ofvalues, and wherein the first intent is associated with a scoreindicating a higher confidence in the first intent than the secondintent; displaying a user interface that presents a first semanticrepresentation comprising the first intent and a first value of thefirst plurality of values, wherein the user interface is configured to:allow a user to select the second intent, wherein selection of thesecond intent causes execution of a second function corresponding to thesecond intent instead of a first function corresponding to the firstintent; and allow a user to select a second value from a list, whereinthe list includes at least a portion of the first plurality of valuesand does not include any values that are not included in the firstplurality of values, and wherein selection of the second value causesexecution of the first function corresponding to the first intent andthe second value; receiving input representing selection of the secondintent; executing the second function based at least partly on the inputrepresenting selection of the second intent; and based at least partlyon the input representing the selection of the second intent, modifyingmetadata for a token of the natural language understanding model toindicate that the token is associated with the second intent, whereinthe token corresponds to a portion of the user speech.
 12. Thecomputer-implemented method of claim 11, wherein displaying the userinterface comprises presenting the first plurality of values in an ordercorresponding to a likelihood that each value of the first plurality ofvalues corresponds to the user speech.
 13. The computer-implementedmethod of claim 12, wherein displaying the user interface comprisespresenting a second semantic representation.
 14. Thecomputer-implemented method of claim 11, wherein displaying the userinterface comprises presenting the first and second semanticrepresentations in an order corresponding to a likelihood that each ofthe first semantic representation and second semantic representationcorresponds to the user speech.
 15. The computer-implemented method ofclaim 11, wherein displaying the user interface comprises presenting anormalized carrier phrase corresponding to the first intent.